Abstract
In this paper, the mobility features of fire to eliminate the interference of similar fire scenes such as lighting by using the change of fire coordinates before and after the fire video in the thermal power plant were proposed to address the issues of interference lookalike fire scenes in the recognition approach. The structure used in this paper for training and testing was the Caffe framework after considering a lot of open-source frameworks for deep learning. After images were taken from several thermal videos, 92% accuracy of performance was obtained. The system was able to differentiate between the false positive fire and non-fire regions with high accuracy. The experiment's outcome indicated that this proposed system could identify, locate and recognize images of fire. The method identifies and localizes fire images for unlike fire situations with good generalization and anti-interference ability.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Chen, T.H., Wu, P.H., Chiou, Y.C.: An early fire-detection method based on image processing. In: 2004 International Conference on Image Processing, ICIP 2004, vol. 3, pp. 1707–1710. IEEE (2004)
Celik, T., Demirel, H.: Fire detection in video sequences using a generic color model. Fire Saf. J. 44(2), 147–158 (2009)
Mueller, M., Karasev, P., Kolesov, I., Tannenbaum, A.: Optical flow estimation for flame detection in videos. IEEE Trans. Image Process. 22(7), 2786–2797 (2013)
Foggia, P., Saggese, A., Vento, M.: Real-time fire detection for video-surveillance applications using a combination of experts based on color, shape, and motion. IEEE Trans. Circuits Syst. Video Technol. 25(9), 1545–1556 (2015)
Zhang, Q., Xu, J., Xu, L., Guo, H.: Deep convolutional neural networks for forest fire detection. In: 2016 International Forum on Management, Education and Information Technology Application. Atlantis Press (2016)
Fu, T.J., Zheng, C.E., Tian, Y., et al.: Forest fire identification based on deep convolutional neural network under complex background. Comput. Modern. 3, 52–57 (2016)
Shao, L., Liu, L., Li, X.: Feature learning for image classification via multiobjective genetic programming. IEEE Trans. Neural Netw. Learn. Syst. 25(7), 1359–1371 (2013)
Xue, X., Wu, X., Jiang, C., Mao, G., Zhu, H.: Integrating sensor ontologies with global and local alignment extractions. Wirel. Commun. Mobile Comput. 2021, 1–10 (2021)
Lin, F.C., Liu, Y.H., Zhang, D.F., et al.: The design of intelligent road sign recognition system based on deep learning. Appl. Electron. Technol. 44(6), 68–71 (2018)
Chang, K.-C., Chu, K.-C., Wang, H.-C., Lin, Y.-C., Pan, J.-S.: Agent-based middleware framework using distributed CPS for improving resource utilization in smart city. Future Gener. Comput. Syst. 108, 445–453 (2020)
Ma, Z.N., Han, Y.J., Peng, L.Y., et al.: Pruning optimization based on deep convolutionalneural network. Appl. Electron. Technol. 44(12), 119–122, 126 (2018)
Chang, K.C., Chu, K.C., Wang, H.C., Lin, Y.C., Pan, J.S.: Energy saving technology of 5G base station based on internet of things collaborative control. IEEE Access 8, 32935–32946 (2020)
Tian, H., Chang, K.-C., Chen, J.S.: Application of hyperbolic partial differential equations in global optimal scheduling of UAV. Alexandria Eng. J. 59(4), 2283–2289 (2020)
Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., Berg, A.C.: SSD: single shot multibox detector. In European Conference on Computer Vision, pp. 21–37. Springer, Cham (2016)
Chu, K.C., Chang, K.C., Wang, H.C., Lin, Y.C., Hsu, T.L.: Field-programmable gate array-based hardware design of optical fiber transducer integrated platform. J. Nanoelectron. Optoelectron. 15(5), 663–671 (2020)
Chu, K.C., Horng, D.J., Chang, K.C.: Numerical optimization of the energy consumption for wireless sensor networks based on an improved ant colony algorithm. IEEE Access 7, 105562–105571 (2019)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Chang, FH. et al. (2021). Convolutional Neural Network for Fire Video Image Detection in the Thermal Power Plant. In: Hassanien, A.E., et al. Proceedings of the International Conference on Artificial Intelligence and Computer Vision (AICV2021). AICV 2021. Advances in Intelligent Systems and Computing, vol 1377. Springer, Cham. https://doi.org/10.1007/978-3-030-76346-6_11
Download citation
DOI: https://doi.org/10.1007/978-3-030-76346-6_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-76345-9
Online ISBN: 978-3-030-76346-6
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)